Overview

Dataset statistics

Number of variables14
Number of observations179
Missing cells13
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.5 KiB
Average record size in memory163.0 B

Variable types

Categorical1
Numeric13

Alerts

Alcohol is highly overall correlated with Color intensity and 2 other fieldsHigh correlation
Color intensity is highly overall correlated with Alcohol and 1 other fieldsHigh correlation
Flavanoids is highly overall correlated with Hue and 5 other fieldsHigh correlation
Hue is highly overall correlated with Flavanoids and 2 other fieldsHigh correlation
Magnesium is highly overall correlated with Proline High correlation
Malic acid is highly overall correlated with HueHigh correlation
Nonflavanoid phenols is highly overall correlated with FlavanoidsHigh correlation
OD280/OD315 of diluted wines is highly overall correlated with Flavanoids and 3 other fieldsHigh correlation
Proanthocyanins is highly overall correlated with Flavanoids and 2 other fieldsHigh correlation
Proline is highly overall correlated with Alcohol and 2 other fieldsHigh correlation
Total phenols is highly overall correlated with Flavanoids and 3 other fieldsHigh correlation
WineClass is highly overall correlated with Alcohol and 6 other fieldsHigh correlation

Reproduction

Analysis started2024-04-03 01:47:59.589901
Analysis finished2024-04-03 01:48:35.912460
Duration36.32 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

WineClass
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
2
71 
1
59 
3
48 
------WebKitFormBoundarybW4UlmUbkiVTekyU--
 
1

Length

Max length42
Median length1
Mean length1.2290503
Min length1

Characters and Unicode

Total characters220
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row3
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 71
39.7%
1 59
33.0%
3 48
26.8%
------WebKitFormBoundarybW4UlmUbkiVTekyU-- 1
 
0.6%

Length

2024-04-03T02:48:36.197496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T02:48:36.544890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2 71
39.7%
1 59
33.0%
3 48
26.8%
webkitformboundarybw4ulmubkivtekyu 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 71
32.3%
1 59
26.8%
3 48
21.8%
- 8
 
3.6%
U 3
 
1.4%
b 3
 
1.4%
o 2
 
0.9%
k 2
 
0.9%
y 2
 
0.9%
r 2
 
0.9%
Other values (16) 20
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 179
81.4%
Lowercase Letter 23
 
10.5%
Uppercase Letter 10
 
4.5%
Dash Punctuation 8
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 3
13.0%
o 2
8.7%
k 2
8.7%
y 2
8.7%
r 2
8.7%
m 2
8.7%
i 2
8.7%
e 2
8.7%
t 1
 
4.3%
u 1
 
4.3%
Other values (4) 4
17.4%
Uppercase Letter
ValueCountFrequency (%)
U 3
30.0%
W 2
20.0%
F 1
 
10.0%
B 1
 
10.0%
K 1
 
10.0%
V 1
 
10.0%
T 1
 
10.0%
Decimal Number
ValueCountFrequency (%)
2 71
39.7%
1 59
33.0%
3 48
26.8%
4 1
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 187
85.0%
Latin 33
 
15.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 3
 
9.1%
b 3
 
9.1%
o 2
 
6.1%
k 2
 
6.1%
y 2
 
6.1%
r 2
 
6.1%
m 2
 
6.1%
i 2
 
6.1%
e 2
 
6.1%
W 2
 
6.1%
Other values (11) 11
33.3%
Common
ValueCountFrequency (%)
2 71
38.0%
1 59
31.6%
3 48
25.7%
- 8
 
4.3%
4 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 71
32.3%
1 59
26.8%
3 48
21.8%
- 8
 
3.6%
U 3
 
1.4%
b 3
 
1.4%
o 2
 
0.9%
k 2
 
0.9%
y 2
 
0.9%
r 2
 
0.9%
Other values (16) 20
 
9.1%

Alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)70.8%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean13.000618
Minimum11.03
Maximum14.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-03T02:48:36.898481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum11.03
5-th percentile11.6585
Q112.3625
median13.05
Q313.6775
95-th percentile14.2215
Maximum14.83
Range3.8
Interquartile range (IQR)1.315

Descriptive statistics

Standard deviation0.81182654
Coefficient of variation (CV)0.062445227
Kurtosis-0.85249957
Mean13.000618
Median Absolute Deviation (MAD)0.68
Skewness-0.051482331
Sum2314.11
Variance0.65906233
MonotonicityNot monotonic
2024-04-03T02:48:37.693464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.05 6
 
3.4%
12.37 6
 
3.4%
12.08 5
 
2.8%
12.29 4
 
2.2%
12.42 3
 
1.7%
12 3
 
1.7%
12.25 3
 
1.7%
12.33 2
 
1.1%
12.72 2
 
1.1%
12.93 2
 
1.1%
Other values (116) 142
79.3%
ValueCountFrequency (%)
11.03 1
0.6%
11.41 1
0.6%
11.45 1
0.6%
11.46 1
0.6%
11.56 1
0.6%
11.61 1
0.6%
11.62 1
0.6%
11.64 1
0.6%
11.65 1
0.6%
11.66 1
0.6%
ValueCountFrequency (%)
14.83 1
0.6%
14.75 1
0.6%
14.39 1
0.6%
14.38 2
1.1%
14.37 1
0.6%
14.34 1
0.6%
14.3 1
0.6%
14.23 1
0.6%
14.22 2
1.1%
14.21 1
0.6%

Malic acid
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)74.7%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.3363483
Minimum0.74
Maximum5.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-03T02:48:38.088506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile1.061
Q11.6025
median1.865
Q33.0825
95-th percentile4.4555
Maximum5.8
Range5.06
Interquartile range (IQR)1.48

Descriptive statistics

Standard deviation1.1171461
Coefficient of variation (CV)0.47815905
Kurtosis0.29920668
Mean2.3363483
Median Absolute Deviation (MAD)0.52
Skewness1.0396512
Sum415.87
Variance1.2480154
MonotonicityNot monotonic
2024-04-03T02:48:38.395196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.73 7
 
3.9%
1.67 4
 
2.2%
1.81 4
 
2.2%
1.53 3
 
1.7%
1.51 3
 
1.7%
1.35 3
 
1.7%
1.9 3
 
1.7%
1.68 3
 
1.7%
1.61 3
 
1.7%
3.59 2
 
1.1%
Other values (123) 143
79.9%
ValueCountFrequency (%)
0.74 1
0.6%
0.89 1
0.6%
0.9 1
0.6%
0.92 1
0.6%
0.94 2
1.1%
0.98 1
0.6%
0.99 1
0.6%
1.01 1
0.6%
1.07 1
0.6%
1.09 1
0.6%
ValueCountFrequency (%)
5.8 1
0.6%
5.65 1
0.6%
5.51 1
0.6%
5.19 1
0.6%
5.04 1
0.6%
4.95 1
0.6%
4.72 1
0.6%
4.61 1
0.6%
4.6 1
0.6%
4.43 1
0.6%

Ash
Real number (ℝ)

Distinct79
Distinct (%)44.4%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.3665169
Minimum1.36
Maximum3.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-03T02:48:38.724969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.92
Q12.21
median2.36
Q32.5575
95-th percentile2.7415
Maximum3.23
Range1.87
Interquartile range (IQR)0.3475

Descriptive statistics

Standard deviation0.27434401
Coefficient of variation (CV)0.11592734
Kurtosis1.1439782
Mean2.3665169
Median Absolute Deviation (MAD)0.16
Skewness-0.17669932
Sum421.24
Variance0.075264635
MonotonicityNot monotonic
2024-04-03T02:48:39.137583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.28 7
 
3.9%
2.3 7
 
3.9%
2.36 6
 
3.4%
2.32 6
 
3.4%
2.7 6
 
3.4%
2.2 5
 
2.8%
2.48 5
 
2.8%
2.38 5
 
2.8%
2.62 4
 
2.2%
2.1 4
 
2.2%
Other values (69) 123
68.7%
ValueCountFrequency (%)
1.36 1
 
0.6%
1.7 2
1.1%
1.71 1
 
0.6%
1.75 1
 
0.6%
1.82 1
 
0.6%
1.88 1
 
0.6%
1.9 1
 
0.6%
1.92 3
1.7%
1.94 1
 
0.6%
1.95 1
 
0.6%
ValueCountFrequency (%)
3.23 1
0.6%
3.22 1
0.6%
2.92 1
0.6%
2.87 1
0.6%
2.86 1
0.6%
2.84 1
0.6%
2.8 1
0.6%
2.78 1
0.6%
2.75 1
0.6%
2.74 2
1.1%

Alcalinity of ash
Real number (ℝ)

Distinct63
Distinct (%)35.4%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean19.494944
Minimum10.6
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-03T02:48:39.475474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile14.77
Q117.2
median19.5
Q321.5
95-th percentile25
Maximum30
Range19.4
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.3395638
Coefficient of variation (CV)0.1713041
Kurtosis0.48794154
Mean19.494944
Median Absolute Deviation (MAD)2.05
Skewness0.21304689
Sum3470.1
Variance11.152686
MonotonicityNot monotonic
2024-04-03T02:48:39.782261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 15
 
8.4%
16 11
 
6.1%
21 11
 
6.1%
18 10
 
5.6%
19 9
 
5.0%
21.5 8
 
4.5%
18.5 7
 
3.9%
19.5 7
 
3.9%
22 7
 
3.9%
22.5 7
 
3.9%
Other values (53) 86
48.0%
ValueCountFrequency (%)
10.6 1
0.6%
11.2 1
0.6%
11.4 1
0.6%
12 1
0.6%
12.4 1
0.6%
13.2 1
0.6%
14 2
1.1%
14.6 1
0.6%
14.8 1
0.6%
15 2
1.1%
ValueCountFrequency (%)
30 1
 
0.6%
28.5 2
 
1.1%
27 1
 
0.6%
26.5 1
 
0.6%
26 1
 
0.6%
25.5 1
 
0.6%
25 5
2.8%
24.5 3
1.7%
24 5
2.8%
23.6 1
 
0.6%

Magnesium
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)29.8%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean99.741573
Minimum70
Maximum162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-03T02:48:40.079593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile80.85
Q188
median98
Q3107
95-th percentile124.3
Maximum162
Range92
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.282484
Coefficient of variation (CV)0.14319489
Kurtosis2.1049913
Mean99.741573
Median Absolute Deviation (MAD)10
Skewness1.0981911
Sum17754
Variance203.98934
MonotonicityNot monotonic
2024-04-03T02:48:40.538118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 13
 
7.3%
86 11
 
6.1%
101 9
 
5.0%
98 9
 
5.0%
96 8
 
4.5%
102 7
 
3.9%
112 6
 
3.4%
94 6
 
3.4%
85 6
 
3.4%
103 5
 
2.8%
Other values (43) 98
54.7%
ValueCountFrequency (%)
70 1
 
0.6%
78 3
 
1.7%
80 5
 
2.8%
81 1
 
0.6%
82 1
 
0.6%
84 3
 
1.7%
85 6
3.4%
86 11
6.1%
87 3
 
1.7%
88 13
7.3%
ValueCountFrequency (%)
162 1
0.6%
151 1
0.6%
139 1
0.6%
136 1
0.6%
134 1
0.6%
132 1
0.6%
128 1
0.6%
127 1
0.6%
126 1
0.6%
124 1
0.6%

Total phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)54.5%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.2951124
Minimum0.98
Maximum3.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-03T02:48:40.810188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.38
Q11.7425
median2.355
Q32.8
95-th percentile3.2745
Maximum3.88
Range2.9
Interquartile range (IQR)1.0575

Descriptive statistics

Standard deviation0.62585105
Coefficient of variation (CV)0.27268863
Kurtosis-0.83562652
Mean2.2951124
Median Absolute Deviation (MAD)0.505
Skewness0.086638586
Sum408.53
Variance0.39168954
MonotonicityNot monotonic
2024-04-03T02:48:41.081231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 8
 
4.5%
2.8 6
 
3.4%
2.6 6
 
3.4%
3 6
 
3.4%
2 5
 
2.8%
2.95 5
 
2.8%
1.65 4
 
2.2%
2.85 4
 
2.2%
1.38 4
 
2.2%
2.45 4
 
2.2%
Other values (87) 126
70.4%
ValueCountFrequency (%)
0.98 1
 
0.6%
1.1 1
 
0.6%
1.15 1
 
0.6%
1.25 1
 
0.6%
1.28 1
 
0.6%
1.3 1
 
0.6%
1.35 1
 
0.6%
1.38 4
2.2%
1.39 2
1.1%
1.4 2
1.1%
ValueCountFrequency (%)
3.88 1
 
0.6%
3.85 1
 
0.6%
3.52 1
 
0.6%
3.5 1
 
0.6%
3.4 1
 
0.6%
3.38 1
 
0.6%
3.3 3
1.7%
3.27 1
 
0.6%
3.25 2
1.1%
3.2 1
 
0.6%

Flavanoids
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)74.2%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.0292697
Minimum0.34
Maximum5.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-03T02:48:41.381978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.5455
Q11.205
median2.135
Q32.875
95-th percentile3.4975
Maximum5.08
Range4.74
Interquartile range (IQR)1.67

Descriptive statistics

Standard deviation0.99885869
Coefficient of variation (CV)0.4922257
Kurtosis-0.88038155
Mean2.0292697
Median Absolute Deviation (MAD)0.835
Skewness0.025343553
Sum361.21
Variance0.99771867
MonotonicityNot monotonic
2024-04-03T02:48:41.847968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.65 4
 
2.2%
0.6 3
 
1.7%
1.25 3
 
1.7%
2.03 3
 
1.7%
2.68 3
 
1.7%
0.58 3
 
1.7%
0.68 2
 
1.1%
1.84 2
 
1.1%
1.36 2
 
1.1%
0.83 2
 
1.1%
Other values (122) 151
84.4%
ValueCountFrequency (%)
0.34 1
0.6%
0.47 2
1.1%
0.48 1
0.6%
0.49 1
0.6%
0.5 2
1.1%
0.51 1
0.6%
0.52 1
0.6%
0.55 1
0.6%
0.56 1
0.6%
0.57 1
0.6%
ValueCountFrequency (%)
5.08 1
0.6%
3.93 1
0.6%
3.75 1
0.6%
3.74 1
0.6%
3.69 1
0.6%
3.67 1
0.6%
3.64 1
0.6%
3.56 1
0.6%
3.54 1
0.6%
3.49 1
0.6%

Nonflavanoid phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)21.9%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.36185393
Minimum0.13
Maximum0.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-03T02:48:42.103096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.19
Q10.27
median0.34
Q30.4375
95-th percentile0.6
Maximum0.66
Range0.53
Interquartile range (IQR)0.1675

Descriptive statistics

Standard deviation0.12445334
Coefficient of variation (CV)0.34393253
Kurtosis-0.63719106
Mean0.36185393
Median Absolute Deviation (MAD)0.085
Skewness0.45015134
Sum64.41
Variance0.015488634
MonotonicityNot monotonic
2024-04-03T02:48:42.332545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.26 11
 
6.1%
0.43 11
 
6.1%
0.29 10
 
5.6%
0.32 9
 
5.0%
0.4 8
 
4.5%
0.27 8
 
4.5%
0.3 8
 
4.5%
0.34 8
 
4.5%
0.37 8
 
4.5%
0.53 7
 
3.9%
Other values (29) 90
50.3%
ValueCountFrequency (%)
0.13 1
 
0.6%
0.14 2
 
1.1%
0.17 5
2.8%
0.19 2
 
1.1%
0.2 2
 
1.1%
0.21 6
3.4%
0.22 6
3.4%
0.24 7
3.9%
0.25 2
 
1.1%
0.26 11
6.1%
ValueCountFrequency (%)
0.66 1
 
0.6%
0.63 4
2.2%
0.61 3
1.7%
0.6 3
1.7%
0.58 3
1.7%
0.56 1
 
0.6%
0.55 1
 
0.6%
0.53 7
3.9%
0.52 5
2.8%
0.5 5
2.8%

Proanthocyanins
Real number (ℝ)

HIGH CORRELATION 

Distinct101
Distinct (%)56.7%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1.5908989
Minimum0.41
Maximum3.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-03T02:48:42.569153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.73
Q11.25
median1.555
Q31.95
95-th percentile2.709
Maximum3.58
Range3.17
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.57235886
Coefficient of variation (CV)0.35977074
Kurtosis0.55464852
Mean1.5908989
Median Absolute Deviation (MAD)0.38
Skewness0.51713717
Sum283.18
Variance0.32759467
MonotonicityNot monotonic
2024-04-03T02:48:42.849403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.35 9
 
5.0%
1.46 7
 
3.9%
1.87 6
 
3.4%
1.25 5
 
2.8%
1.98 4
 
2.2%
1.56 4
 
2.2%
1.66 4
 
2.2%
2.08 4
 
2.2%
1.62 3
 
1.7%
2.29 3
 
1.7%
Other values (91) 129
72.1%
ValueCountFrequency (%)
0.41 1
0.6%
0.42 2
1.1%
0.55 1
0.6%
0.62 1
0.6%
0.64 2
1.1%
0.68 1
0.6%
0.73 2
1.1%
0.75 1
0.6%
0.8 2
1.1%
0.81 1
0.6%
ValueCountFrequency (%)
3.58 1
 
0.6%
3.28 1
 
0.6%
2.96 1
 
0.6%
2.91 2
1.1%
2.81 3
1.7%
2.76 1
 
0.6%
2.7 1
 
0.6%
2.5 1
 
0.6%
2.49 1
 
0.6%
2.45 1
 
0.6%

Color intensity
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)74.2%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean5.0580899
Minimum1.28
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-03T02:48:43.129817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile2.114
Q13.22
median4.69
Q36.2
95-th percentile9.598
Maximum13
Range11.72
Interquartile range (IQR)2.98

Descriptive statistics

Standard deviation2.3182859
Coefficient of variation (CV)0.45833228
Kurtosis0.38152227
Mean5.0580899
Median Absolute Deviation (MAD)1.51
Skewness0.86858479
Sum900.34
Variance5.3744494
MonotonicityNot monotonic
2024-04-03T02:48:43.379005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.8 4
 
2.2%
4.6 4
 
2.2%
2.6 4
 
2.2%
2.9 3
 
1.7%
5 3
 
1.7%
2.8 3
 
1.7%
5.1 3
 
1.7%
5.6 3
 
1.7%
5.7 3
 
1.7%
3.05 3
 
1.7%
Other values (122) 145
81.0%
ValueCountFrequency (%)
1.28 1
0.6%
1.74 1
0.6%
1.9 1
0.6%
1.95 2
1.1%
2 1
0.6%
2.06 2
1.1%
2.08 1
0.6%
2.12 1
0.6%
2.15 1
0.6%
2.2 1
0.6%
ValueCountFrequency (%)
13 1
0.6%
11.75 1
0.6%
10.8 1
0.6%
10.68 1
0.6%
10.52 1
0.6%
10.26 1
0.6%
10.2 1
0.6%
9.899999 1
0.6%
9.7 1
0.6%
9.58 1
0.6%

Hue
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)43.8%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.95744944
Minimum0.48
Maximum1.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-03T02:48:43.648169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.57
Q10.7825
median0.965
Q31.12
95-th percentile1.2845
Maximum1.71
Range1.23
Interquartile range (IQR)0.3375

Descriptive statistics

Standard deviation0.22857157
Coefficient of variation (CV)0.23872965
Kurtosis-0.34409574
Mean0.95744944
Median Absolute Deviation (MAD)0.165
Skewness0.021091272
Sum170.426
Variance0.052244961
MonotonicityNot monotonic
2024-04-03T02:48:43.945781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.04 8
 
4.5%
1.23 7
 
3.9%
1.12 6
 
3.4%
0.89 5
 
2.8%
0.96 5
 
2.8%
0.57 5
 
2.8%
1.25 5
 
2.8%
0.7 4
 
2.2%
0.86 4
 
2.2%
1.09 4
 
2.2%
Other values (68) 125
69.8%
ValueCountFrequency (%)
0.48 1
 
0.6%
0.54 1
 
0.6%
0.55 1
 
0.6%
0.56 2
 
1.1%
0.57 5
2.8%
0.58 2
 
1.1%
0.59 2
 
1.1%
0.6 3
1.7%
0.61 2
 
1.1%
0.62 1
 
0.6%
ValueCountFrequency (%)
1.71 1
 
0.6%
1.45 1
 
0.6%
1.42 1
 
0.6%
1.38 1
 
0.6%
1.36 2
 
1.1%
1.33 1
 
0.6%
1.31 2
 
1.1%
1.28 2
 
1.1%
1.27 1
 
0.6%
1.25 5
2.8%

OD280/OD315 of diluted wines
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)68.5%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.6116854
Minimum1.27
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-03T02:48:44.239528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.4625
Q11.9375
median2.78
Q33.17
95-th percentile3.58
Maximum4
Range2.73
Interquartile range (IQR)1.2325

Descriptive statistics

Standard deviation0.70999043
Coefficient of variation (CV)0.27185144
Kurtosis-1.0864345
Mean2.6116854
Median Absolute Deviation (MAD)0.52
Skewness-0.3072855
Sum464.88
Variance0.50408641
MonotonicityNot monotonic
2024-04-03T02:48:44.475924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.87 5
 
2.8%
1.82 4
 
2.2%
3 4
 
2.2%
2.78 4
 
2.2%
1.75 3
 
1.7%
2.31 3
 
1.7%
3.17 3
 
1.7%
1.56 3
 
1.7%
2.96 3
 
1.7%
2.77 3
 
1.7%
Other values (112) 143
79.9%
ValueCountFrequency (%)
1.27 1
 
0.6%
1.29 2
1.1%
1.3 1
 
0.6%
1.33 3
1.7%
1.36 1
 
0.6%
1.42 1
 
0.6%
1.47 1
 
0.6%
1.48 1
 
0.6%
1.51 2
1.1%
1.55 1
 
0.6%
ValueCountFrequency (%)
4 1
0.6%
3.92 1
0.6%
3.82 1
0.6%
3.71 1
0.6%
3.69 1
0.6%
3.64 1
0.6%
3.63 1
0.6%
3.59 1
0.6%
3.58 2
1.1%
3.57 1
0.6%

Proline
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)68.0%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean746.89326
Minimum278
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-03T02:48:44.729345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile354.55
Q1500.5
median673.5
Q3985
95-th percentile1297.25
Maximum1680
Range1402
Interquartile range (IQR)484.5

Descriptive statistics

Standard deviation314.90747
Coefficient of variation (CV)0.42162313
Kurtosis-0.24840311
Mean746.89326
Median Absolute Deviation (MAD)202.5
Skewness0.76782178
Sum132947
Variance99166.717
MonotonicityNot monotonic
2024-04-03T02:48:45.029860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
520 5
 
2.8%
680 5
 
2.8%
625 4
 
2.2%
630 4
 
2.2%
750 4
 
2.2%
562 3
 
1.7%
660 3
 
1.7%
480 3
 
1.7%
450 3
 
1.7%
1285 3
 
1.7%
Other values (111) 141
78.8%
ValueCountFrequency (%)
278 1
0.6%
290 1
0.6%
312 1
0.6%
315 1
0.6%
325 1
0.6%
342 1
0.6%
345 2
1.1%
352 1
0.6%
355 1
0.6%
365 1
0.6%
ValueCountFrequency (%)
1680 1
0.6%
1547 1
0.6%
1515 1
0.6%
1510 1
0.6%
1480 1
0.6%
1450 1
0.6%
1375 1
0.6%
1320 1
0.6%
1310 1
0.6%
1295 1
0.6%

Interactions

2024-04-03T02:48:31.174831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:00.241765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:02.572829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:04.995681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:07.501813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:10.266075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:12.464901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:14.991677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:17.173687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:19.696550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:22.509740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:24.789759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:27.217122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:31.560100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:00.342994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:02.749827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:05.242619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:07.701913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:10.465619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:12.714869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:15.191957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:17.353242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:19.889015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:22.780650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:24.982702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:27.362474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:31.958462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:00.509712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:02.910358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:05.408805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:07.924165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:10.640762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:12.937435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:15.298038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:17.528926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:20.087581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:22.945785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:25.157070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:27.549674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:32.429393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:00.795228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:03.088172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:05.629276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:08.113642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:10.815099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:13.195742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:15.465632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:17.923075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:20.261845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:23.125373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:25.545863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:27.741317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:32.681632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:01.043072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:03.280012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:05.872139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:08.307051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:10.996872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:13.403463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:15.660989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:18.213439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:20.471530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:23.328696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:25.763242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:28.113847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:32.875352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:01.209168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:03.437013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:06.041229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:08.479130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:11.154560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:13.588407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:15.802775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:18.371476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:20.783107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:23.477647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:25.925419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:28.517070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:33.055387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:01.372175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:03.605237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:06.238805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:08.677130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:11.306395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:13.797943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:15.992078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:18.559773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:20.998404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:23.654645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:26.086712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:28.908737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:33.234411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:01.542184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:03.762451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:06.436884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:08.844411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:11.462177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:13.977965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:16.167261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:18.736847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:21.160261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:23.812709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:26.248634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:29.183653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:33.423334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:01.699192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:03.938336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:06.621250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:09.266398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:11.620484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:14.139527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:16.293741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:18.892832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:21.312630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:23.978430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:26.383262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:29.431468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:33.683282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:01.895051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:04.113004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:06.813853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:09.501688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:11.785062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:14.302901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:16.446563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:19.066457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:21.483835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:24.136125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:26.559337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:29.730294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:33.925262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:02.066831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:04.263458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:07.005799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:09.687142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:11.957068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:14.461456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:16.618229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:19.230357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:21.666059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:24.295338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:26.715357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:30.117833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:34.189980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:02.234870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:04.483908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:07.175402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:09.865541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:12.117329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:14.619447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:16.787209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:19.351376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:21.844434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:24.448161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:26.888326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:30.424947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:34.370958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:02.388836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:04.752564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:07.322263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:10.063800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:12.282367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:14.795231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:16.982912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:19.536351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:22.055894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:24.618754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:27.058459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T02:48:30.823133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-04-03T02:48:45.254300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Alcalinity of ashAlcoholAshColor intensityFlavanoidsHueMagnesiumMalic acidNonflavanoid phenolsOD280/OD315 of diluted winesProanthocyaninsProlineTotal phenolsWineClass
Alcalinity of ash1.000-0.3070.366-0.074-0.444-0.353-0.1700.3040.389-0.326-0.254-0.456-0.3770.382
Alcohol-0.3071.0000.2440.6350.295-0.0240.3660.140-0.1620.1030.1930.6340.3110.581
Ash0.3660.2441.0000.2830.079-0.0500.3610.2310.146-0.0070.0240.2530.1320.222
Color intensity-0.0740.6350.2831.000-0.043-0.4190.3570.2900.060-0.318-0.0310.4570.0110.649
Flavanoids-0.4440.2950.079-0.0431.0000.5350.233-0.325-0.5440.7420.7300.4300.8790.752
Hue-0.353-0.024-0.050-0.4190.5351.0000.036-0.560-0.2680.4850.3430.2080.4390.583
Magnesium-0.1700.3660.3610.3570.2330.0361.0000.080-0.2370.0570.1740.5080.2460.403
Malic acid0.3040.1400.2310.290-0.325-0.5600.0801.0000.255-0.255-0.245-0.057-0.2800.496
Nonflavanoid phenols0.389-0.1620.1460.060-0.544-0.268-0.2370.2551.000-0.495-0.385-0.270-0.4480.355
OD280/OD315 of diluted wines-0.3260.103-0.007-0.3180.7420.4850.057-0.255-0.4951.0000.5540.2530.6870.645
Proanthocyanins-0.2540.1930.024-0.0310.7300.3430.174-0.245-0.3850.5541.0000.3080.6670.399
Proline-0.4560.6340.2530.4570.4300.2080.508-0.057-0.2700.2530.3081.0000.4190.644
Total phenols-0.3770.3110.1320.0110.8790.4390.246-0.280-0.4480.6870.6670.4191.0000.560
WineClass0.3820.5810.2220.6490.7520.5830.4030.4960.3550.6450.3990.6440.5601.000

Missing values

2024-04-03T02:48:34.690859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-03T02:48:35.200419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

WineClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
0312.853.272.5822.0106.01.650.600.600.965.580.872.11570.0
1212.641.362.0216.8100.02.021.410.530.625.750.981.59450.0
2212.081.132.5124.078.02.001.580.401.402.201.312.72630.0
3212.001.512.4222.086.01.451.250.501.633.601.052.65450.0
4113.643.102.5615.2116.02.703.030.171.665.100.963.36845.0
5114.102.022.4018.8103.02.752.920.322.386.201.072.751060.0
6212.292.832.2218.088.02.452.250.251.992.151.153.30290.0
7113.731.502.7022.5101.03.003.250.292.385.701.192.711285.0
8313.715.652.4520.595.01.680.610.521.067.700.641.74740.0
9211.410.742.5021.088.02.482.010.421.443.081.102.31434.0
WineClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
169314.341.682.7025.098.02.801.310.532.7013.000.571.96660.0
170113.741.672.2516.4118.02.602.900.211.625.850.923.201060.0
171213.491.662.2424.087.01.881.840.271.033.740.982.78472.0
172313.693.262.5420.0107.01.830.560.500.805.880.961.82680.0
173313.523.172.7223.597.01.550.520.500.554.350.892.06520.0
174114.371.952.5016.8113.03.853.490.242.187.800.863.451480.0
175312.703.552.3621.5106.01.701.200.170.845.000.781.29600.0
176113.581.662.3619.1106.02.863.190.221.956.901.092.881515.0
177114.062.152.6117.6121.02.602.510.311.255.051.063.581295.0
178------WebKitFormBoundarybW4UlmUbkiVTekyU--NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN